/*
 * Javlov - a Java toolkit for reinforcement learning with multi-agent support.
 * 
 * Copyright (c) 2009 Matthijs Snel
 * 
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */
package net.javlov;

public class ActorCriticAgent extends TDAgent {

	protected Actor actor;
	
	/**
	 * Constructs TD agent with the given actor, tabular value function
	 * and gamma = 0.9.
	 */
	public ActorCriticAgent(Actor act) {
		actor = act;
		setPolicy(act);
	}
	
	/**
	 * Constructs agent with the given actor and value function and gamma = 0.9.
	 * 
	 * @param act the actor.
	 * @param v the value function to use.
	 */
	public ActorCriticAgent(Actor act, ValueFunction v) {
		this(act, v, 0.9);
	}
	
	/**
	 * Constructs agent with given actor, value function and gamma.
	 * 
	 * @param act the actor.
	 * @param v the value function to use.
	 * @param gamma the discountfactor gamma e [0, 1].
	 */
	public ActorCriticAgent(Actor act, ValueFunction v, double gamma) {
		super(v, gamma);
		actor = act;
		setPolicy(act);
	}
	
	@Override
	public <T> Action doStep( State<T> s, double reward ) {
		Action a = super.doStep(s, reward);
		actor.update(getLastTDError());
		return a;
	}

}
